Microbial single-cell analysis in picoliter-sized batch cultivation chambers

Microfluidic single-cell scale-down systems: introduction, application, and future challenges

Performance losses during the scaling-up of bioprocesses from the laboratory to the production scale are common obstacles caused by the formation of concentration gradients in bioreactors. To overcome these obstacles, so-called scale-down bioreactors are used to analyze selected large-scale conditions and are one of the most important predictive tools for the successful transfer of bioprocesses from the lab to the industrial scale. In this regard, cellular behavior is usually measured as an averaged value, neglecting possible cell-to-cell heterogeneity within the culture. In contrast, microfluidic single-cell cultivation (MSCC) systems offer the possibility of understanding cellular processes on a single-cell level. To date, most MSCC systems have a limited choice of cultivation parameters that are not representative of bioprocess-relevant environmental conditions. Herein, we critically review recent advances in MSCC that allow the cultivation and analysis of cells under dynamic (bioprocess-relevant) environmental conditions. Finally, we discuss what technological advances and efforts are needed to bridge the gap between current MSCC systems and the use of these systems as single-cell scale-down devices.

Non-inhibitory levels of oxygen during cultivation increase freeze-drying stress tolerance in Limosilactobacillus reuteri DSM 17938

The physiological effects of oxygen on Limosilactobacillus reuteri DSM 17938 during cultivation and the ensuing properties of the freeze-dried probiotic product was investigated. On-line flow cytometry and k-means clustering gating was used to follow growth and viability in real time during cultivation. The bacterium tolerated aeration at 500 mL/min, with a growth rate of 0.74 ± 0.13 h^-1 which demonstrated that low levels of oxygen did not influence the growth kinetics of the bacterium. Modulation of the redox metabolism was, however, seen already at non-inhibitory oxygen levels by 1.5-fold higher production of acetate and 1.5-fold lower ethanol production. A significantly higher survival rate in the freeze-dried product was observed for cells cultivated in presence of oxygen compared to absence of oxygen (61.8% ± 2.4% vs. 11.5% ± 4.3%), coinciding with a higher degree of unsaturated fatty acids (UFA:SFA ratio of 10 for air sparged vs. 3.59 for N₂ sparged conditions.). Oxygen also resulted in improved bile tolerance and boosted 5'nucleotidase activity (370 U/L vs. 240 U/L in N₂ sparged conditions) but lower tolerance to acidic conditions compared bacteria grown under complete anaerobic conditions which survived up to 90 min of exposure at pH 2. Overall, our results indicate the controlled supply of oxygen during production may be used as means for probiotic activity optimization of L. reuteri DSM 17938.

microbeSEG: A deep learning software tool with OMERO data management for efficient and accurate cell segmentation

In biotechnology, cell growth is one of the most important properties for the characterization and optimization of microbial cultures. Novel live-cell imaging methods are leading to an ever better understanding of cell cultures and their development. The key to analyzing acquired data is accurate and automated cell segmentation at the single-cell level. Therefore, we present microbeSEG, a user-friendly Python-based cell segmentation tool with a graphical user interface and OMERO data management. microbeSEG utilizes a state-of-the-art deep learning-based segmentation method and can be used for instance segmentation of a wide range of cell morphologies and imaging techniques, e.g., phase contrast or fluorescence microscopy. The main focus of microbeSEG is a comprehensible, easy, efficient, and complete workflow from the creation of training data to the final application of the trained segmentation model. We demonstrate that accurate cell segmentation results can be obtained within 45 minutes of user time. Utilizing public segmentation datasets or pre-labeling further accelerates the microbeSEG workflow. This opens the door for accurate and efficient data analysis of microbial cultures.

Microfluidic dose-response platform to track the dynamics of drug response in single mycobacterial cells

Preclinical analysis of drug efficacy is critical for drug development. However, conventional bulk-cell assays statically assess the mean population behavior, lacking resolution on drug-escaping cells. Inaccurate estimation of efficacy can lead to overestimation of compounds, whose efficacy will not be confirmed in the clinic, or lead to rejection of valuable candidates. Time-lapse microfluidic microscopy is a powerful approach to characterize drugs at high spatiotemporal resolution, but hard to apply on a large scale. Here we report the development of a microfluidic platform based on a pneumatic operating principle, which is scalable and compatible with long-term live-cell imaging and with simultaneous analysis of different drug concentrations. We tested the platform with mycobacterial cells, including the tubercular pathogen, providing the first proof of concept of a single-cell dose-response assay. This dynamic in-vitro model will prove useful to probe the fate of drug-stressed cells, providing improved predictions of drug efficacy in the clinic.

Communities of Niche-optimized Strains (CoNoS) - Design and creation of stable, genome-reduced co-cultures

Current bioprocesses for production of value-added compounds are mainly based on pure cultures that are composed of rationally engineered strains of model organisms with versatile metabolic capacities. However, in the comparably well-defined environment of a bioreactor, metabolic flexibility provided by various highly abundant biosynthetic enzymes is much less required and results in suboptimal use of carbon and energy sources for compound production. In nature, non-model organisms have frequently evolved in communities where genome-reduced, auxotrophic strains cross-feed each other, suggesting that there must be a significant advantage compared to growth without cooperation. To prove this, we started to create and study synthetic communities of niche-optimized strains (CoNoS) that consists of two strains of the same species Corynebacterium glutamicum that are mutually dependent on one amino acid. We used both the wild-type and the genome-reduced C1* chassis for introducing selected amino acid auxotrophies, each based on complete deletion of all required biosynthetic genes. The best candidate strains were used to establish several stably growing CoNoS that were further characterized and optimized by metabolic modelling, microfluidic experiments and rational metabolic engineering to improve amino acid production and exchange. Finally, the engineered CoNoS consisting of an l-leucine and l-arginine auxotroph showed a specific growth rate equivalent to 83% of the wild type in monoculture, making it the fastest co-culture of two auxotrophic C. glutamicum strains to date. Overall, our results are a first promising step towards establishing improved biobased production of value-added compounds using the CoNoS approach.

Assessing the growth kinetics and stoichiometry of Escherichia coli at the single-cell level

Microfluidic cultivation and single-cell analysis are inherent parts of modern microbial biotechnology and microbiology. However, implementing biochemical engineering principles based on the kinetics and stoichiometry of growth in microscopic spaces remained unattained. We here present a novel integrated framework that utilizes distinct microfluidic cultivation technologies and single-cell analytics to make the fundamental math of process-oriented biochemical engineering applicable at the single-cell level. A combination of non-invasive optical cell mass determination with sub-pg sensitivity, microfluidic perfusion cultivations for establishing physiological steady-states, and picoliter batch reactors, enabled the quantification of all physiological parameters relevant to approximate a material balance in microfluidic reaction environments. We determined state variables (biomass concentration based on single-cell dry weight and mass density), biomass synthesis rates, and substrate affinities of cells grown in microfluidic environments. Based on this data, we mathematically derived the specific kinetics of substrate uptake and growth stoichiometry in glucose-grown Escherichia coli with single-cell resolution. This framework may initiate microscale material balancing beyond the averaged values obtained from populations as a basis for integrating heterogeneous kinetic and stoichiometric single-cell data into generalized bioprocess models and descriptions.

Microfluidic Reproduction of Dynamic Bioreactor Environment Based on Computational Lifelines

The biotechnological production of fine chemicals, proteins and pharmaceuticals is usually hampered by loss of microbial performance during scale-up. This challenge is mainly caused by discrepancies between homogeneous environmental conditions at laboratory scale, where bioprocesses are optimized, and inhomogeneous conditions in large-scale bioreactors, where production takes place. Therefore, to improve strain selection and process development, it is of great interest to characterize these fluctuating conditions at large-scale and to study their effects on microbial cells. In this paper, we demonstrate the potential of computational fluid dynamics (CFD) simulation of large-scale bioreactors combined with dynamic microfluidic single-cell cultivation (dMSCC). Environmental conditions in a 200 L bioreactor were characterized with CFD simulations. Computational lifelines were determined by combining simulated turbulent multiphase flow, mass transport and particle tracing. Glucose availability for Corynebacterium glutamicum cells was determined. The reactor was simulated with average glucose concentrations of 6 g m−3, 10 g m−3 and 16 g m−3. The resulting computational lifelines, discretized into starvation and abundance regimes, were used as feed profiles for the dMSCC to investigate how varying glucose concentration affects cell physiology and growth rate. In this study, each colony in the dMSCC device represents a single cell as it travels through the reactor. Under oscillating conditions reproduced in the dMSCC device, a decrease in growth rate of about 40% was observed compared to continuous supply with the same average glucose availability. The presented approach provides insights into environmental conditions observed by microorganisms in large-scale bioreactors. It also paves the way for an improved understanding of how inhomogeneous environmental conditions influence cellular physiology, growth and production.

Microbial factories: monitoring vitamin B2 production by Escherichia coli in microfluidic cultivation chambers

Microbial cells represent a standard production host for various important biotechnological products. Production yields can be increased by optimising strains and growth conditions and understanding deviations in production rates over time or within the microbial population. We introduce here microfluidic cultivation chambers for highly parallel studies on microbial cultures, enabling continuous biosynthesis monitoring of the industrially relevant product by Escherichia coli cells. The growth chambers are defined by ring-valves that encapsulate a volume of 200 pL when activated. Bacterial cells, labelled with magnetic beads, are inoculated in a small magnetic trap, positioned in the centre of each chamber. Afterwards, the ring-valves are partially activated, allowing for exchange reagents, such as the addition of fresh media or specific inducers of biosynthesis, while the bacterial cells and their progeny are maintained inside. On this platform, we monitor the production of riboflavin (vitamin B₂). We used different variants of a riboflavin-overproducing bacterial strain with different riboflavin production levels and could distinguish them on the level of individual micro-colonies. In addition, we could also observe differences in the bacterial morphology with respect to the production. The presented platform represents a flexible microfluidic tool for further studies of microbial cell factories.

Next-Generation Antimicrobial Resistance Surveillance System Based on the Internet-of-Things and Microfluidic Technique

Antimicrobial resistance (AMR) of foodborne pathogens is a global crisis in public health and economic growth. A real-time surveillance system is key to track the emergence of AMR bacteria and provides a comprehensive AMR trend from farm to fork. However, current AMR surveillance systems, which integrate results from multiple laboratories using the conventional broth microdilution method, are labor-intensive and time-consuming. To address these challenges, we present the internet of things (IoT), including colorimetric-based microfluidic sensors, a custom-built portable incubator, and machine learning algorithms, to monitor AMR trends in real time. As a top priority microbe that poses risks to human health, Campylobacter was selected as a bacterial model to demonstrate and validate the IoT-assisted AMR surveillance. Image classification with convolution neural network ResNet50 on the colorimetric sensors achieved an accuracy of 99.5% in classifying bacterial growth/inhibition patterns. The IoT was used to carry out a small-scale survey study, identifying eight Campylobacter isolates out of 35 chicken samples. A 96% agreement on Campylobacter AMR profiles was achieved between the results from the IoT and the conventional broth microdilution method. The data collected from the intelligent sensors were transmitted from local computers to a cloud server, facilitating real-time data collection and integration. A web browser was developed to demonstrate the spatial and temporal AMR trends to end-users. This rapid, cost-effective, and portable approach is able to monitor, assess, and mitigate the burden of bacterial AMR in the agri-food chain.

Real-Time Respiration Changes as a Viability Indicator for Rapid Antibiotic Susceptibility Testing in a Microfluidic Chamber Array

Rapid identification of a pathogen and the measurement of its antibiotic susceptibility are key elements in the diagnostic process of bacterial infections. Microfluidic technologies offer great control over handling and manipulation of low sample volumes with the possibility to study microbial cultures on the single-cell level. Downscaling the dimensions of cultivation systems directly results in a lower number of bacteria required for antibiotic susceptibility testing (AST) and thus in a reduction of the time to result. The developed platform presented in this work allows the reading of pathogen resistance profiles within 2-3 h based on the changes of dissolved oxygen levels during bacterial cultivation. The platform contains hundreds of individual growth chambers prefilled with a hydrogel containing oxygen-sensing nanoprobes and different concentrations of antibiotic compounds. The performance of the developed platform is tested using quality control Escherichia coli strains (ATCC 25922 and ATCC 35218) in response to clinically relevant antibiotics. The results are in agreement with values given in reference guidelines and independent measurements using a clinical AST protocol. Finally, the platform is successfully used for the AST of an E. coli clinical isolate obtained from a patient blood culture.

(Optochemical) Control of Synthetic Microbial Coculture Interactions on a Microcolony Level

Synthetic microbial cocultures carry enormous potential for applied biotechnology and are increasingly the subject of fundamental research. So far, most cocultures have been designed and characterized based on bulk cultivations without considering the potentially highly heterogeneous and diverse single-cell behavior. However, an in-depth understanding of cocultures including their interacting single cells is indispensable for the development of novel cultivation approaches and control of cocultures. We present the development, validation, and experimental characterization of an optochemically controllable bacterial coculture on a microcolony level consisting of two Corynebacterium glutamicum strains. Our coculture combines an l-lysine auxotrophic strain together with a l-lysine-producing variant carrying the genetically IPTG-mediated induction of l-lysine production. We implemented two control approaches utilizing IPTG as inducer molecule. First, unmodified IPTG was supplemented to the culture enabling a medium-based control of the production of l-lysine, which serves as the main interacting component. Second, optochemical control was successfully performed by utilizing photocaged IPTG activated by appropriate illumination. Both control strategies were validated studying cellular growth on a microcolony level. The novel microfluidic single-cell cultivation strategies applied in this work can serve as a blueprint to validate cellular control strategies of synthetic mono- and cocultures with single-cell resolution at defined environmental conditions.